Evidence map›Paper›PMID 25716827›Full record

ArticleGenome biology and evolution2015

Insights on the emergence of Mycobacterium tuberculosis from the analysis of Mycobacterium kansasii.

Joyce Wang, Fiona McIntosh, Nicolas Radomski, Ken Dewar, Roxane Simeone, Jost Enninga, Roland Brosch, Eduardo P Rocha, Frédéric J Veyrier, Marcel A Behr

Open access · goldAbstract read
In one paragraph

Article in Genome biology and evolution, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
7.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

55 citing papers in PubMed, 88 citations in OpenAlex.

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  10. Review
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  14. Review
  15. Review
  16. Review
  17. Insights into the ancestry evolution of theNAR genomics and bioinformatics · 2021
    Article
  18. Article
  19. Pathogenic Determinants of theMicroorganisms · 2021
    Article
  20. Control of Toxin-Antitoxin Systems by Proteases inFrontiers in molecular biosciences · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 2 countries.

Joyce WangDepartment of Microbiology and Immunology, McGill University, Montreal, Québec, Canada McGill International TB Centre, Montreal, Québec, Canada.
Fiona McIntoshMcGill International TB Centre, Montreal, Québec, Canada Research Institute of the McGill University Health Centre, Montreal, Québec, Canada.
Nicolas RadomskiMcGill International TB Centre, Montreal, Québec, Canada Research Institute of the McGill University Health Centre, Montreal, Québec, Canada.
Ken DewarMcGill University and Génome Québec Innovation Center, Montreal, Québec, Canada.
Roxane SimeoneUnit for Integrated Mycobacterial Pathogenomics, Institut Pasteur, Paris, France.
Jost EnningaDynamics of Host-Pathogen Interactions Unit, Institut Pasteur, Paris, France.
Roland BroschUnit for Integrated Mycobacterial Pathogenomics, Institut Pasteur, Paris, France.
Eduardo P RochaMicrobial Evolutionary Genomics Unit, Institut Pasteur, Paris, France.
Frédéric J VeyrierMcGill International TB Centre, Montreal, Québec, Canada INRS-Institut Armand-Frappier, Laval, Québec, Canada frederic.veyrier@iaf.inrs.ca marcel.behr@mcgill.ca.
Marcel A BehrDepartment of Microbiology and Immunology, McGill University, Montreal, Québec, Canada McGill International TB Centre, Montreal, Québec, Canada Department of Medicine, McGill University, Montreal, Québec, Canada frederic.veyrier@iaf.inrs.ca marcel.behr@mcgill.ca.
Institut Pasteur · FRMcGill University · CAMcGill University Health Centre · CAInstitut National de la Recherche Scientifique · CAMcGill University and Génome Québec Innovation Centre · CA

Funding

Canadian Institutes of Health Research MOP-97813
6 · The paper itself

Abstract

By phylogenetic analysis, Mycobacterium kansasii is closely related to Mycobacterium tuberculosis. Yet, although both organisms cause pulmonary disease, M. tuberculosis is a global health menace, whereas M. kansasii is an opportunistic pathogen. To illuminate the differences between these organisms, we have sequenced the genome of M. kansasii ATCC 12478 and its plasmid (pMK12478) and conducted side-by-side in vitro and in vivo investigations of these two organisms. The M. kansasii genome is 6,432,277 bp, more than 2 Mb longer than that of M. tuberculosis H37Rv, and the plasmid contains 144,951 bp. Pairwise comparisons reveal conserved and discordant genes and genomic regions. A notable example of genomic conservation is the virulence locus ESX-1, which is intact and functional in the low-virulence M. kansasii, potentially mediating phagosomal disruption. Differences between these organisms include a decreased predicted metabolic capacity, an increased proportion of toxin-antitoxin genes, and the acquisition of M. tuberculosis-specific genes in the pathogen since their common ancestor. Consistent with their distinct epidemiologic profiles, following infection of C57BL/6 mice, M. kansasii counts increased by less than 10-fold over 6 weeks, whereas M. tuberculosis counts increased by over 10,000-fold in just 3 weeks. Together, these data suggest that M. kansasii can serve as an image of the environmental ancestor of M. tuberculosis before its emergence as a professional pathogen, and can be used as a model organism to study the switch from an environmental opportunistic pathogen to a professional host-restricted pathogen.

Indexed as

Evolution, MolecularAnimalsFemaleGenome, BacterialGenomicsMice, Inbred C57BLMycobacterium kansasiiMycobacterium tuberculosisPhenotypePhylogenyVirulencecomparative genomicsmycobacteriaMycobacterium kansasiiphylogenyvirulence

Identifiers

PMID25716827
PMCPMC5322544
OpenAlexW2113657329

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.